|Title||Harnessing networks and machine learning in neuropsychiatric care.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Cornblath, EJ, Lydon-Staley, DM, Bassett, DS|
|Journal||Curr Opin Neurobiol|
|Date Published||2019 Apr|
The development of next-generation therapies for neuropsychiatric illness will likely rely on a precise and accurate understanding of human brain dynamics. Toward this end, researchers have focused on collecting large quantities of neuroimaging data. For simplicity, we will refer to large cross-sectional neuroimaging studies as broad studies and to intensive longitudinal studies as deep studies. Recent progress in identifying illness subtypes and predicting treatment response in neuropsychiatry has been supported by these study designs, along with methods bridging machine learning and network science. Such methods combine analytic power, interpretability, and direct connection to underlying theory in cognitive neuroscience. Ultimately, we propose a general framework for the treatment of neuropsychiatric illness relying on the findings from broad and deep studies combined with basic cognitive and physiologic measurements.
|Alternate Journal||Curr. Opin. Neurobiol.|